Deep Learning is a class of machine learning algorithms. Deep learning architectures, such as deep neural networks, have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design.
Inference and training, two tools used for deep learning, are tending towards low precision arithmetic. Maximizing throughput of deep learning algorithms and computations may assist in meeting the needs of deep learning processors, for example, those performing deep learning in a data center.
Sparse-dense matrix multiplication (SDMM) operations are useful in a deep learning context. But traditional CPU and GPU instruction set architectures require symmetric inputs having the same density, which limits the ability to gain a performance advantage by taking advantage of the sparsity of a sparse input matrix.